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arXiv · 1808.09058

Quantum enhanced cross-validation for near-optimal neural networks architecture selection

Priscila G. M. dos Santos·Rodrigo S. Sousa·Ismael C. S. Araujo·Adenilton J. da Silva
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Abstract

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory and the possibility to train artificial neural networks in superposition. We obtain an exponential quantum speedup in the evaluation of neural networks. We also verify experimentally through a reduced experimental analysis that the proposed algorithm can be used to select near-optimal neural networks.

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Priscila G. M. dos Santos, Rodrigo S. Sousa, Ismael C. S. Araujo, Adenilton J. da Silva. 2018-08-27. Quantum enhanced cross-validation for near-optimal neural networks architecture selection. https://doi.org/10.1142/s0219749918400051

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